Transform your features into a higher dimensional, sparse space. Then
train a linear model on these features.

First fit an ensemble of trees (totally random trees, a random
forest, or gradient boosted trees) on the training set. Then each leaf
of each tree in the ensemble is assigned a fixed arbitrary feature
index in a new feature space. These leaf indices are then encoded in a
one-hot fashion.

Each sample goes through the decisions of each tree of the ensemble
and ends up in one leaf per tree. The sample is encoded by setting
feature values for these leaves to 1 and the other feature values to 0.

The resulting transformer has then learned a supervised, sparse,
high-dimensional categorical embedding of the data.